[1]NI Huaifa,SHEN Xiaobo,SUN Quansen.Robust canonical correlation analysis based onlow rank decomposition[J].CAAI Transactions on Intelligent Systems,2017,12(4):491-497.[doi:10.11992/tis.201607024]
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Robust canonical correlation analysis based onlow rank decomposition
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 4
Page number:
491-497
Column:
学术论文—人工智能基础
Public date:
2017-08-25
- Title:
- Robust canonical correlation analysis based onlow rank decomposition
- Keywords:
- Pattern recognition; feature extraction; data dimensionality reduction; canonical correlation analysis; low rank representation; low rank decomposition; low rank component; noise component
- CLC:
- TP391
- DOI:
- 10.11992/tis.201607024
- Abstract:
- Canonical correlation analysis (CCA) is a popular multi-feature extraction method, which can effectively explore the correlations between two sets of features. Up to now, CCA has been widely used in pattern recognition, however it has limited feature extraction power for large noisy data. For CCA to deal better with noisy data, a new method, robust canonical correlation analysis (RbCCA), based on low rank decomposition, is proposed. RbCCA first decomposes features using low rank decomposition to get the low rank and noisy components, then it constructs new covariance matrices based on these two components. A discriminative criteria function is further established to obtain discriminative projections by maximizing the correlations of the low rank component and minimizing the correlations of the noisy component. Experimental results on a MFEAT handwritten dataset, and ORL and Yale face datasets show that RbCCA can achieve higher recognition rates than existing CCA methods, especially in noisy settings.
- References:
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Last Update:
2017-08-25